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Adaptation of the CCSDS 123.0-B-2 Standard for RGB Video Compression

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The integration of video sensors on-board satellites is becoming a trend in the space industry, since they provide extra information in the temporal domain when compared with traditional remote sensing… Click to show full abstract

The integration of video sensors on-board satellites is becoming a trend in the space industry, since they provide extra information in the temporal domain when compared with traditional remote sensing imaging acquisition equipment. The inclusion of the temporal dimension together with the constant increase in the sensor resolution supposes a challenge for on-board processing, taking into account the limited computational and storage resources on-board satellites and that it is unfeasible to directly transmit raw video to ground, due to downlink bandwidth limitations. For these reasons, on-board video compression is needed. However, the inherent complexity of the video encoders used on ground limits their implementation on environments with high constraints in terms of computational burden, area, and power consumption. This article proposes an extended compression chain that implements as compression core the CCSDS 123.0-B-2 standard, originally developed for near-lossless compression of multi- and hyperspectral images. In addition, some preprocessing stages are included to manage the temporal dimension of RGB videos efficiently. The proposed solution guarantees low complexity and flexibility to compress both multi- and hyperspectral images and panchromatic and RGB videos by using a single compression instance, which is adapted by adding or removing the appropriate stages. Results demonstrate the viability of this solution to be implemented on space payloads, since high compression ratios are achieved without incurring in a penalty in terms of video quality.

Keywords: compression; video compression; rgb; ccsds 123; video; 123 standard

Journal Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year Published: 2022

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